Chowdhury Sancharee Hom, Chen Lujie Karen, Hu Peter, Badjatia Neeraj, Podell Jamie Erin
University of Maryland Baltimore County.
University of Maryland School of Medicine.
Res Sq. 2024 Sep 2:rs.3.rs-4803007. doi: 10.21203/rs.3.rs-4803007/v1.
Paroxysmal Sympathetic Hyperactivity (PSH) occurs with high prevalence among critically ill Traumatic Brain Injury (TBI) patients and is associated with worse outcomes. The PSH-Assessment Measure (PSH-AM) consists of a Clinical Features Scale (CFS) and a Diagnosis Likelihood Tool (DLT), intended to quantify the severity of sympathetically-mediated symptoms and likelihood that they are due to PSH, respectively, on a daily basis. Here, we aim to identify and explore the value of dynamic trends in the evolution of sympathetic hyperactivity following acute TBI using elements of the PSH-AM.
We performed an observational cohort study of 221 acute critically ill TBI patients for whom daily PSH-AM scores were calculated over the first 14 days of hospitalization. A principled group-based trajectory modeling approach using unsupervised K-means clustering was used to identify distinct patterns of CFS evolution within the cohort. We also evaluated the relationships between trajectory group membership and PSH diagnosis, as well as PSH DLT score, hospital discharge GCS, ICU and hospital length of stay, duration of mechanical ventilation, and mortality. Baseline clinical and demographic features predictive of trajectory group membership were analyzed using univariate screening and multivariate multinomial logistic regression.
We identified four distinct trajectory groups. Trajectory group membership was significantly associated with clinical outcomes including PSH diagnosis and DLT score, ICU length of stay, and duration of mechanical ventilation. Baseline features independently predictive of trajectory group membership included age and post-resuscitation motor GCS.
This study adds to the sparse research characterizing the heterogeneous temporal trends of sympathetic nervous system activation during the acute phase following TBI. This may open avenues for early identification of at-risk patients to receive tailored interventions to limit secondary brain injury associated with autonomic dysfunction and thereby improve TBI patient outcomes.
阵发性交感神经过度兴奋（PSH）在重症创伤性脑损伤（TBI）患者中高发，且与更差的预后相关。PSH评估量表（PSH-AM）由临床特征量表（CFS）和诊断可能性工具（DLT）组成，旨在分别每日量化交感神经介导症状的严重程度以及这些症状由PSH引起的可能性。在此，我们旨在利用PSH-AM的要素识别并探索急性TBI后交感神经过度兴奋演变过程中动态趋势的价值。
我们对221例急性重症TBI患者进行了一项观察性队列研究，在住院的前14天计算其每日PSH-AM评分。使用无监督K均值聚类的基于原则的分组轨迹建模方法来识别队列中CFS演变的不同模式。我们还评估了轨迹组成员与PSH诊断、PSH DLT评分、出院时格拉斯哥昏迷量表（GCS）、重症监护病房（ICU）和住院时间、机械通气时间以及死亡率之间的关系。使用单变量筛选和多变量多项逻辑回归分析预测轨迹组成员的基线临床和人口统计学特征。
我们识别出四个不同的轨迹组。轨迹组成员与临床结局显著相关，包括PSH诊断和DLT评分、ICU住院时间以及机械通气时间。独立预测轨迹组成员的基线特征包括年龄和复苏后运动GCS。
本研究为TBI急性期交感神经系统激活的异质时间趋势这一稀疏研究增添了内容。这可能为早期识别高危患者开辟途径，以便接受量身定制的干预措施，以限制与自主神经功能障碍相关的继发性脑损伤，从而改善TBI患者的预后。
